Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.

ABSTRACT:

Background

Protein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim of this study was to develop and validate an explainable machine learning model to identify the risk of PEW in patients on MHD.

Methods

From January 2024 to June 2024, a total of 908 patients on MHD were enrolled. The data were randomly divided into training (n = 635) and test (n = 273) sets at a 7:3 ratio. The optimal features were selected via least absolute shrinkage and selection operator (LASSO) regression. Seven machine learning (ML) algorithms were used to develop and validate models to predict PEW risk, and the performance

SUBMITTER: Cai G 

PROVIDER: S-EPMC12523148 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature